Arthropod object detection in natural environment has characteristics of complex object background， large scale difference， and dense objects，resulting in poor object detection accuracy and precision. Therefore， an arthropod object detection method was proposed based on the improved Faster RCNN model， namely AROD RCNN （ARthropod Object Detection RCNN）. Firstly， a Supervised Parallel mechanism with Spatial and Channel ATtention modules （SPSCAT） was designed to improve the accuracy of arthropod object detection in the environment with complex background. Then， the second-generation deformable convolution was introduced to reconstruct the convolutional layer with C1~C5 blocks in ResNet50， and the Feature Pyramid Network （FPN） was adopted to perform feature fusion on the C2~C6 blocks in ResNet50 to solve the problem that large difference in object scale affected detection accuracy. Finally， the Dense Local Regression （DLR） method was used to improve the regression stage， thereby improving the accuracy of the model regression. Experimental results show that on ArTaxOr （Arthropod Taxonomy Orders Object Detection） dataset， the proposed method has the mean Average Precision （mAP） of 0.717， which is 0.453 higher than that of the original model， and has the recall reached 0.787. It can be seen that the proposed method can effectively solve the problems of object occlusion and complex background， and performs well in the detection of dense arthropod objects and small arthropod objects.

To solve the problems of low precision and poor generalization performance of traditional image segmentation algorithms on the plant images under bias light in plant factory, a method based on neural network and deep learning for accurately segmenting the plant images under artificial bias light in plant factory was proposed. By using this method, the segmentation accuracy on the original test set of bias light plant images is 91.89% and is far superior to that by other segmentation algorithms such as Fully Convolutional Network (FCN), clustering, threshold and region growth. In addition, this method has better segmentation effect and generalization performance than the above methods on plant images under different color lights. The experimental results show that the proposed method can significantly improve the accuracy of plant image segmentation under bias light, and can be applied to practical plant factory projects.

The new method processing mobile real-time read-only transactions was proposed in mobile broadcast environments. Various multiversion broadcast disk organizations were introduced. Mobile read-only transactions could be committed with no-blocking by multiversion mechanism. The conflicts between mobile read-only and mobile update transactions could be eliminated by optimistic method. To avoid unnecessary restarts of transactions, multiversion dynamic adjustment of serialization order was adopted. If a readonly transaction passed all the backward validation in MH, it could be committed without contacting with the server. Response time of mobile read-only transactions was greatly reduced. The results of simulation experiment show that the new method performs better than other protocols.

Expatiates explains how to adopt RTUML methods in firmware analysis,development,implementation and testing under Rhapsody platform through the example of SESI-II neon light controller.A new method is discovered in developing firmware system,which is called Real Time UML,different from the traditional flow-based C language method.